



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case     :Dict			       =logging.get_logger(__name__)

__snake_case     :Union[str, Any]			       ={
    'funnel-transformer/small': 'https://huggingface.co/funnel-transformer/small/resolve/main/config.json',
    'funnel-transformer/small-base': 'https://huggingface.co/funnel-transformer/small-base/resolve/main/config.json',
    'funnel-transformer/medium': 'https://huggingface.co/funnel-transformer/medium/resolve/main/config.json',
    'funnel-transformer/medium-base': 'https://huggingface.co/funnel-transformer/medium-base/resolve/main/config.json',
    'funnel-transformer/intermediate': (
        'https://huggingface.co/funnel-transformer/intermediate/resolve/main/config.json'
    ),
    'funnel-transformer/intermediate-base': (
        'https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/config.json'
    ),
    'funnel-transformer/large': 'https://huggingface.co/funnel-transformer/large/resolve/main/config.json',
    'funnel-transformer/large-base': 'https://huggingface.co/funnel-transformer/large-base/resolve/main/config.json',
    'funnel-transformer/xlarge': 'https://huggingface.co/funnel-transformer/xlarge/resolve/main/config.json',
    'funnel-transformer/xlarge-base': 'https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/config.json',
}



class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     Union[str, Any]								=     'funnel'
				A_   :     List[Any]								=     {
				    'hidden_size': 'd_model',
				    'num_attention_heads': 'n_head',
				}


				def __init__(							self      :  List[str]						, __UpperCamelCase      :  int=30_522						, __UpperCamelCase      :  Optional[Any]=[4, 4, 4]						, __UpperCamelCase      :  str=None						, __UpperCamelCase      :  List[str]=2						, __UpperCamelCase      :  Dict=768						, __UpperCamelCase      :  int=12						, __UpperCamelCase      :  Optional[Any]=64						, __UpperCamelCase      :  List[Any]=3_072						, __UpperCamelCase      :  Dict="gelu_new"						, __UpperCamelCase      :  Optional[Any]=0.1						, __UpperCamelCase      :  List[str]=0.1						, __UpperCamelCase      :  List[Any]=0.0						, __UpperCamelCase      :  int=0.1						, __UpperCamelCase      :  Optional[int]=None						, __UpperCamelCase      :  Optional[Any]=1e-9						, __UpperCamelCase      :  str="mean"						, __UpperCamelCase      :  Dict="relative_shift"						, __UpperCamelCase      :  int=True						, __UpperCamelCase      :  Optional[int]=True						, __UpperCamelCase      :  Dict=True						, **__UpperCamelCase      :  List[str]						, )		->     Optional[int]:
											A      						=    vocab_size
											A      						=    block_sizes
											A      						=    [1] * len(__UpperCamelCase       ) if block_repeats is None else block_repeats
											assert len(__UpperCamelCase       ) == len(
											    self.block_repeats       ), "`block_sizes` and `block_repeats` should have the same length."
											A      						=    num_decoder_layers
											A      						=    d_model
											A      						=    n_head
											A      						=    d_head
											A      						=    d_inner
											A      						=    hidden_act
											A      						=    hidden_dropout
											A      						=    attention_dropout
											A      						=    activation_dropout
											A      						=    initializer_range
											A      						=    initializer_std
											A      						=    layer_norm_eps
											assert pooling_type in [
											    "mean",
											    "max",
											], f'''Got {pooling_type} for `pooling_type` but only \'mean\' and \'max\' are supported.'''
											A      						=    pooling_type
											assert attention_type in [
											    "relative_shift",
											    "factorized",
											], f'''Got {attention_type} for `attention_type` but only \'relative_shift\' and \'factorized\' are supported.'''
											A      						=    attention_type
											A      						=    separate_cls
											A      						=    truncate_seq
											A      						=    pool_q_only

											super().__init__(**__UpperCamelCase       )


				@property
				def __UpperCamelCase						(							self      :  Dict       )		->     Tuple:
											return sum(self.block_sizes       )


				@num_hidden_layers.setter
				def __UpperCamelCase						(							self      :  int						, __UpperCamelCase      :  List[str]       )		->     Any:
											raise NotImplementedError(
											    'This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`.'       )


				@property
				def __UpperCamelCase						(							self      :  int       )		->     List[Any]:
											return len(self.block_sizes       )


				@num_blocks.setter
				def __UpperCamelCase						(							self      :  Union[str, Any]						, __UpperCamelCase      :  Optional[Any]       )		->     Optional[Any]:
											raise NotImplementedError('This model does not support the setting of `num_blocks`. Please set `block_sizes`.'       )







'''simple docstring'''







from math import isqrt, loga


def       __UpperCamelCase					(      lowercase_       :  int   ):
       """simple docstring"""




       a_						     =			[True] * max_number
       for i in range(2   ,       isqrt(max_number - 1   ) + 1   ):
              if is_prime[i]:
                     for j in range(i**2   ,       lowercase_   ,       lowercase_   ):
                            a_						     =			False

       return [i for i in range(2   ,       lowercase_   ) if is_prime[i]]


def       __UpperCamelCase					(      lowercase_       :  int = 800_800   ,       lowercase_       :  int = 800_800   ):
       """simple docstring"""




       a_						     =			degree * loga(lowercase_   )
       a_						     =			int(lowercase_   )
       a_						     =			calculate_prime_numbers(lowercase_   )

       a_						     =			0
       a_						     =			0
       a_						     =			len(lowercase_   ) - 1
       while left < right:
              while (
                  prime_numbers[right] * loga(prime_numbers[left]   )
                  + prime_numbers[left] * loga(prime_numbers[right]   )
                  > upper_bound
              ):
                     right -= 1
              hybrid_integers_count += right - left
              left += 1

       return hybrid_integers_count


if __name__ == "__main__":
  print(f"""{solution() = }""")




